Diet
Assessment
Food Frequency Questionnaire (FFQ)
Food Frequency Questionnaires (FFQs) assess an individual’s typical food consumption over a specific period (ranging from weeks to years), asking about the frequency and portion size of foods and beverages. They are usually administered by an enumerator and help estimate long-term dietary intake of nutrients for research purposes.
Justification
FFQs are ideal for studying long-term dietary habits and diet-related non-communicable diseases (NCDs), especially in urban areas, as they capture extended exposure to unhealthy diets. This also makes them useful for large-scale epidemiological surveys.
Type of data
FFQs can be tailored to assess specific dietary components, foods, or populations. Participants report the frequency of food consumption (e.g., daily, weekly) and may estimate portion sizes to calculate nutrient intake. The tool categorizes foods by group, tracks portion sizes (standard or relative), and evaluates dietary patterns and nutrient intake. Portion sizes can be asked separately from how often they consume a food (frequency), which may be more accurate, but it can increase survey length and respondent burdens. For low-literacy settings, combining questions of portion sizes using images with frequencies may be helpful.
Pros
- FFQs assess overall dietary intake and temporal changes, capture individual diet patterns, and be easier to implement than 24-hour recalls when the food list is shorter. They typically take 30-60 minutes to complete, depending on the length of the list.
- FFQs can be moderately easy and inexpensive to administer but require some literacy from respondents.
- Many FFQs are self-administered and used in higher income settings. However, they can also be customized to suit the target population or specific dietary questions.
Adapt the FFQ food list to urban and LMIC settings:
- Link food environment assessment to feed into the FFQ food list, such as using the In-depth Vendor Assessment (Availability) tool, which can give an idea of the types of foods that are available locally, including foods that are common in urban areas, but might not be a part of FFQs in other contexts (e.g. street foods). However, it is key to think ahead about timing and sequencing of data collection, as the Vendor assessment must be conducted well in advance of the FFQ to provide time for data processing and analysis and adaptation of the FFQ food list.
- Make sure to consider local dietary patterns in options for frequency (e.g., daily, monthly) as well as cultural considerations such as festivals or holiday eating patterns (e.g., Ramadan). Addressing literacy issues through visual aids or pictorial FFQs can assist respondents, in addition to inclusion of local or colloquial food names.
Cons
- FFQs do not weigh or quantify foods but they do include portion size estimation (unless you use a qualitative FFQ – see more in the low-resource section below), which leads to lower accuracy compared to other methods. They can impose a high cognitive burden on respondents and may result in overreporting.
- FFQs are not ideal for cross-cultural comparisons and may take longer than methods like 24-hour recalls. While useful for studying long-term dietary patterns in cohort studies or epidemiological research, shorter, lower-burden methods are better for population monitoring.
- FFQs may exclude culturally-specific foods, be influenced by weekday variations, and are imprecise for absolute nutrient intake. They are more suited to case-control and retrospective studies, which don’t require tracking day-to-day variations. A longer food list can improve accuracy.
- Additionally, FFQs are designed in a way that may not best capture foods eaten away from home, such as those from restaurants or street vendors, which is particularly relevant in urban areas with diverse food choices, high frequency of eating meals away from home, and cultural diversity. These gaps can affect the accuracy of dietary assessments in such settings and in rapidly changing urban food environments – and it is hard to update food lists in timely fashion. Also, information on recipes, mixed dishes and foods eaten together (eating occasions) is lacking.
Indicators
If not using a food composition table:
If targeting overall diet quality in a broad population, the GDQS provides acomprehensive assessment of diet quality by evaluating both the diversity of food groups and the pattern of healthy and unhealthy foods in the diet, making it appropriate to evaluate overall dietary patterns in diverse populations,
The GDQS score measures nutrient adequacy and NCD risk (see GDQS Toolkit), and can be easily calculated using the GDQS app. The score can also be calculated from quantitative dietary data gathered through other methods, such as 24-hour recall or by use of the GDQS app. The GDQS metric has been validated as a proxy for nutrient adequacy and NCD risk outcomes for diet. The score (ranging from 0 – 49) has no universal cutoff, but categorical risk levels can be generated (scores <15, ≥15 and <23, and ≥23 respectively).
- Comprehensive food-based metric with simple scoring of quantities
- Can be used to characterize population-level dietary patterns
- See Tabulation guidelines
GDQS (+): The GDQS positive (+) is the total score as a sum of 16 healthy GDQS food groups (score ranges from 0-32). A higher score indicates a higher relative contribution of healthy foods to the diet.
GDQS (-): The GDQS negative (-) is the total score as a sum of 7 unhealthy food groups and 2 food groups considered unhealthy when consumed in excessive quantities (high fat dairy, red meat) (score from 0-17). A lower score shows a higher relative contribution of unhealthy foods to the diet.
A recent study suggests these composite metrics may have limited equivalence across contexts, and that use of sub-metrics may be more informative.
If targeting WRA or young children, use the Minimum Dietary Diversity (MDD-W and MDD-IYCF):
Minimum Dietary Diversity (MDD-W and MDD-IYCF)
The MDD-W and MDD-IYC aresimpler to use and more focused on nutrient diversity, which is critical for women of reproductive age and young children. The MDD-W is designed for women (15-49), male or female children and adolescents (4-15 years old) in LMICs for proxy population indicator for micronutrient adequacy of diets (Diop et al 2025), MDD-IYC (6–23 months). The MDD-W has been used in some cases with adult men and has been validated in certain settings as a proxy for micronutrient adequacy in men (Gomez et al 2024), but this is not widely done. The GDQS would be a better measure for men.
If using a food composition table:
Daily intakes of specific food groups, foods, and nutrients
Dietary intake data must be processed using the FCT for the country being studied. Suggested indicators of interest in urban settings to track unhealthy food consumption could include dietary energy density (kilocalories/grams) and dietary content of free sugars (as a percentage of total energy intake). Dietary energy density is calculated by dividing the energy content (in kcal) by weight of foods (in grams) eaten, and in some contexts, free sugars (added sugars to foods and beverages) can be calculated.
These indicators estimate the percentage of the population that is at risk for nutrient inadequacy for each individual nutrient. Food intake data is converted into nutrient intake using the FCT, while reference values such as the Estimated Average Requirement (EAR) cut-point method (caution: cannot be used for iron in women and children).
Summary measures such as the Mean Probability of Adequacy (MPA)
The MPA is a composite indicator that assesses nutrient adequacy and the likelihood of meeting nutrient needs. The MPA adjusts for variations within the individual, giving a probability of adequacy for each person and averages this across the population. The MPA has an adjustment for variability in nutrient intake (in contrast to mean nutrient intake (MNI) or mean adequacy ratio (MAR), which do not), intra subject variability especially where nutrient deficiencies (or excesses, particularly in urban settings) or adequacy are a problem, nutrient imbalances are common, and diets change frequently. MPA is also recommended for evaluating the effectiveness of nutrition interventions.
Additional notes on indicators
The FFQ can be used to calculate other commonly used indicators of diet quality such as the Diet Diversity Scores (DDS), the Household Dietary Diversity Score (HDDS), among others.
The FFQ food list should include detailed itemization of snacks, packaged foods, and sugary drinks as urban populations often have higher consumption of those foods. In addition, the FFQ should probe for multiple food sources, as urban populations may eat foods from street vendors, supermarkets, food delivery and other sources in contrast to traditional rural settings where most food is cooked at home.
A quantitative FFQ can be used to calculate the intake of specific nutrients that are over- or under-consumed in the population. Quantitative intake data on macro- and micronutrients is needed as well as the use of a food composition table and conversion factors, such as that provided by a 24-hour dietary recall, or a weighed food record.
An FFQ can be used to calculate indicators such as the quantity of food groups consumed but not all screeners can do so – they must be coupled with appropriate reference data to provide intake estimates.
Tool and indicator validation
Validation is essential in determining the suitability of a dietary assessment instrument, focusing on its validity, misreporting and measurement errors. Validity assesses how accurately the instrument reflects actual intake, usually in comparison with other methods. Misreporting, influenced by factors like social desirability or memory limitations, can impact accuracy. Measurement errors, either systematic (bias) or random, affect the reliability of findings. Every dietary assessment method has its own set of potential biases and errors – no method is perfect.
FFQs have been validated for specific settings in terms of culturally-specific adaptations and assessing its validity compared to other dietary assessment methods such as the 24HR recall (e.g., Ethiopia, Peru) and in some cases for alternative methods of administration (e.g., an online FFQ compared to face-to-face (FTF) administration. FFQs should not be used if they not validated as an instrument for the country and context (e.g., in Sri Lanka, China, Rwanda, Vietnam). Unfortunately, there are not a wide range of FFQs validated for use in LMICs.
Low-resource adaptations
- In urban environments, where food choices are diverse, frequently changing, and influenced by a range of factors, dietary screeners (see register of validated short dietary assessment instruments) offer a practical, efficient, and less burdensome alternative to FFQs. These screeners provide a low-cost way to estimate dietary intake while focusing on specific food groups rather than the overall diet. With shorter recall periods, they improve accuracy by reducing cognitive burden and better capturing frequent food changes. They are particularly useful for reporting food consumed away from home (FAFH), which is often underreported in traditional FFQs. However, dietary screeners are not always as accurate and require locally validated reference data. Shorter recall periods help improve accuracy, especially if respondents have difficulty remembering what they ate due to daily changes in food choices.
- Additionally, screeners are easy to implement for rapid data collection and are better to use in urban settings where food availability is typically less seasonal. Screeners can easily be adapted to culturally-specific foods or habits. Digital screeners also facilitate ease of use, enhancing their practicality.
- A screener should capture convenience foods, fast food, and low-cost processed foods that may be more commonly consumed. Screeners can capture dietary patterns associated with these health issues, such as high-calorie or high-sodium food consumption as well as dietary modifications related to diet-related NCDs and chronic disease.
- Screeners can be administered in person, phone or online – providing options for urban populations.
- Targeted short FFQs focusing on specific micronutrients or on specific populations of interest (i.e., folate intake assessments, calcium intake, dietary fiber)
- Qualitative FFQs, which only measure the frequencies of foods consumed and not their quantities. Portion sizes are standardized, thus there is a loss of accuracy but there is a lower burden for respondents. Alternatively, a semi-quantitative FFQ can be used, which asks questions on portion sizes as well. However, this means many of the recommended indicators cannot be calculated. You could potentially calculate the GDQS using the frequency of food groups consumed which would allow for an estimate of dietary quality based on food group frequency or show diet patterns but not as accurate as a quantitative assessment.
- Shortened food list (less than 100 food items)
High-resource adaptations
- Conduct a pilot test of the FFQ in the geographic areas of interest to refine questions and food lists, and to capture changes in food environments such as the growth of food delivery services.
- Increase the reference period or periodicity of data collection (e.g., to capture seasonal and/or temporal changes in diets)
- Include visual aids such as household measurements (e.g., cups), photographs food models, or standardized references or recipes, ask about cooking methods and added ingredients. You can also conduct pilots on portion sizes to better understand what works best in the local context.
- Expand the food list to include a more diverse and comprehensive set of items, particularly if interested in specific or less common food items, which improves accuracy, as well as brands or restaurant foods that are common to the area of interest – which are more common in urban areas, or additional items like fortified foods, dietary supplements, food trends or diets and health challenges. Also make sure to include different types of beverages, including separating out those with added sugars (e.g. sugar-sweetened beverages), which are commonly consumed in urban areas and contribute to growing overweight and obesity prevalence.
- Expanding geographic scope – such as adding a rural population group for comparison. However, you will likely need a separate FFQ for the rural population, as there may be differences in food availability and consumption patterns.
Sampling and data collection considerations
The sampling approach depends on the user’s question of interest and target population, but it is crucial to ensure a study’s sample is representative of the target population. The two primary sampling approaches are probability and non-probability sampling. There are several methods of probability sampling, including simple random sampling, where any member of the target population has an equal chance of being selected into the study, interval sampling, in which people of the targeted group are continually available and selected into a sample (i.e., consumers in a market), and stratified sampling, which divides the target population into groups for sampling, and/or cluster sampling which uses groupings from which the sample population is selected.
In urban settings, administrative boundaries and enumeration areas can help organize sampling. In many countries, lists of enumeration areas can be acquired, after which a sample frame or list of households or targeted individuals from each of those areas are developed, from which households or individuals are sampled. Correcting for over- or under sampling through sample weighting is essential to improve data accuracy. If the question of interest is to assess changes at population-level in dietary quality due to a program or policy, it is critical that the sampling frame include populations that have been exposed to those interventions. Non-probability sampling methods, such as convenience and snowball sampling, can be used when ease of access is prioritized.
Careful conceptualization of the relationship between food environments and diets helps guide geographic focus and sampling strategy, ensuring more meaningful and representative results. For example, if your question of interest is to compare between areas of differing levels of urbanization, the geographic frame could include urban, peri-urban and rural areas, and a sampling strategy would need to select a representative sample of households and individuals, or by stratifying by different types of food environments.
The FFQ should be locally adapted, reflecting commonly consumed foods in urban areas, with care to include cultural or traditional foods and processed or convenience foods typical in urban settings. In addition, an appropriate recall period should be used (e.g., past 7 days, 30 days, typical month) that captures habitual dietary patterns and the longer the recall period, the more likely respondents will recall infrequently consumed foods. Food groups must be classified in a way that is appropriate to urban settings, particularly for foods such as street foods or fast foods that are more common in urban than rural settings. Finally, portion sizes must be clearly indicated because in urban settings food portion sizes can vary significantly, this could entail the use of photographs to aid recall.
Other data sources
While it is ideal to collect primary data, real world limitations to data collection in urban settings may prevent this, including on the implementing side (e.g., budget/resource constraints) and in the field (e.g., difficulty in accessing populations of safety issues in dense urban areas, conflict-affected settings). It may be helpful to examine secondary data sources, either as background to inform primary data collection or in place of it, if data collection is not feasible.
| Data sources | Pros | Cons | Indicators |
|---|---|---|---|
| Household consumption and expenditure surveys (HCES) [Household-level consumption] | Low cost, nationally representative Conducted regularly (every 3-5 years) with a large sample Contains other variables such as data on socioeconomic status, education, and other determinants relevant to nutrition Often also includes acquisition data (food acquired from purchases, production, in-kind) | Modules are heterogenous across countries, making comparisons challenging Doesn’t differentiate between subgroups to estimate differences in probability of deficiencies in high-risk groups Household level (no individual dietary data), does not address intra-household allocation issues that may affect household members May have issues with accurately recording food consumed away from home (FAFH) which are very important in urban settings (e.g., street foods, meals consumed at school) | Diet diversity Energy consumption Protein consumption Food consumption |
| Global Dietary Database [Individual-level diets] | Harmonized data (variables, units, food definitions) for individual-level dietary data from nutrition surveys for 188 countries | Need nutrition and data analysis expertise Surveys use different designs and tools Four food categories excluded (e.g., poultry, dairy-based desserts) | Includes 51 dietary factors including 14 foods, 7 beverages, 12 macronutrients, and 18 micronutrients |
| GIFT Database (FAO) [Individual-level dietary diversity] | Data are disaggregated by sex and age Individual quantitative food consumption data coded with the FoodEx2 classification system, data are screened and formatted using R Dashboards presenting indicators and summary statistics on foods and diets Can link food groupings to own dietary data (dataset available upon request) | Need nutrition and data analysis expertise, particularly as outliers and missing data not removed from original datasets and energy and nutrient values are provided directly from surveys (does not link food consumption datasets to food composition data) Data not available for some countries, there are limitations for investigating urban nutrition – many studies did not have this focus Many of the datasets are old, many are not nationally representative and there are no data on statistical weights | Statistics on food consumption can be calculated for individual food items or using the nutrition-sensitive food groups (e.g., sources of micro- and macronutrients in the diet, macronutrient contribution to total intake) Estimated usual intakes of selected nutrients (with Statistical Program to Assess habitual Dietary Exposure tool (SPADE)) MDD-W (and Food group diversity score, individual food group consumption) Food consumption (daily diet g/per person per day, proportion of food groups consumed (%), calories per person per day) Other indicators for food safety (dietary exposure to chemicals) and environmental impacts of food consumption (emission, water and land use) |
| Demographic and Health Surveys
| Nationally representative data on dietary diversity | Need nutrition and data analysis expertise Alternatively, the DHS StatCompiler and mobile app allows for automatic indicator calculation and disaggregation | MDD-W Infant and young child feeding practices (IYCF) (MAD, MDD, MMF) Percentage consuming food group (Pregnant and lactacting women – PLW, women of reproductive age – WRA) |
Examples of urban research using these tools and indicators
Association between neighborhood food environment and dietary quality among adolescents in Kuala Lumpur, Malaysia (Norddin 2025)
Association of the retail food environment, BMI, dietary patterns, and socioeconomic position in urban areas of Mexico (Pineda 2023)
FFQ and Dietary Assessment-Related Resources
- Bailey, R., “Overview of dietary assessment methods for measuring intakes of foods, beverages, and dietary supplements in research studies.” Current opinion in biotechnology, 70 (2021), 91-96.
- Data4Diets: Building Blocks for Diet-related Food Security Analysis, Version 2.0. Tufts University, accessed 2023, https://inddex.nutrition.tufts.edu/data4diets
- INTAKE. Center for Dietary Assessment. FHI 360. Accessed 2024. https://www.intake.org/resources
- National Cancer Institute. Dietary Assessment Primer. National Institutes of Health (NIH). Accessed 2023. https://dietassessmentprimer.cancer.gov/approach/table.html
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